From Book News, Inc.
Larose (statistics, Central Connecticut State University) walks readers step-by-step through the various algorithms and statistical structures that underlie data mining software and presents examples of their operation on actual large data sets. Chapter exercises are included, and screenshots and diagrams encourage graphical learning. The text is appropriate for an introductory course in data mining for advanced undergraduate or graduate students. No previous work in calculus, statistics, computer programming, or databases is required.Copyright © 2004 Book News, Inc., Portland, OR

Book Description
Learn Data Mining by doing data mining
Data mining can be revolutionary-but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.
Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include:
* Data preprocessing and classification
* Exploratory analysis
* Decision trees
* Neural and Kohonen networks
* Hierarchical and k-means clustering
* Association rules
* Model evaluation techniques
Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.

From the Back Cover
Learn Data Mining by doing data mining

Data mining can be revolutionary—but only when it's done right. The powerful black box data mining software now available can produce disastrously misleading results unless applied by a skilled and knowledgeable analyst. Discovering Knowledge in Data: An Introduction to Data Mining provides both the practical experience and the theoretical insight needed to reveal valuable information hidden in large data sets.

Employing a "white box" methodology and with real-world case studies, this step-by-step guide walks readers through the various algorithms and statistical structures that underlie the software and presents examples of their operation on actual large data sets. Principal topics include: Data preprocessing and classification Exploratory analysis Decision trees Neural and Kohonen networks Hierarchical and k-means clustering Association rules Model evaluation techniques

Complete with scores of screenshots and diagrams to encourage graphical learning, Discovering Knowledge in Data: An Introduction to Data Mining gives students in Business, Computer Science, and Statistics as well as professionals in the field the power to turn any data warehouse into actionable knowledge.

About the Author
DANIEL T. LAROSE received his PhD in statistics from the University of Connecticut. An associate professor of statistics at Central Connecticut State University, he developed and directs Data Mining@CCSU, the world's first online master of science program in data mining. He has also worked as a data mining consultant for Connecticut-area companies. He is currently working on the next two books of his three-volume series on Data Mining: Data Mining Methods and Models and Data Mining the Web: Uncovering Patterns in Web Content, scheduled to publish respectively in 2005 and 2006.

FROM THE PUBLISHER